[AISWorld] FGCS special issue (IF: 2.639): Big Data in the Cloud due on May 31 2015

[Chang V.I.]

Apologies if you've received from somewhere. Big Data research in the Cloud is expected to be the hottest topic in the next few years. There are papers demonstrating architectures, applications, services, experiments and simulations in the Cloud to support the cases for Big Data adoption. For organizations that adopt Big Data, the boundary between the use of private clouds, public clouds, Internet of Things (IoT) is sometimes very thin to allow better access, performance and efficiency of analyzing the data and understanding the data analysis. A common approach is to develop Big Data in the Cloud to deliver Everything as a Service. While volume, variety, velocity, veracity and value are the major factors in Big Data systems and applications, there are other challenges to be resolved. We classify all the challenges into nine categories and we seek the best papers, demonstrations, recommendations and solutions in the following areas:

1) Techniques, algorithms and innovative methods of processing Big Data in the Cloud (or Big datasets) that achieve performance, accuracy and low-costs.
2) Big Data Design, implementation, evaluation and services in the Cloud, including the development process, use cases, experiments and associated simulations.
3) Systems and applications developed by Big Data and descriptions of how Big Data can be used in multidisciplines such as bioinformatics, finance and education.
4) Big Data services and applications in natural science, weather science, physics and astronomy.
5) Security, privacy, trust, data ownership and risk simulations for Big Data in the Cloud.
6) Business and economic models (quantitative or computational), social network analyses, scientific workflows and business processes related to Big Data in the Cloud.
7) Big Data in the Cloud integrations with other technologies such as SOA, data mining, machine learning, HPC, cloud storage, multi-clouds and internet of things.
8) Big Data analytics and visualization with new algorithms showing how to achieve significant improvements from existing methods in the Cloud.
9) Adoption cases, frameworks and user evaluations involved with quantitative or computational research methods for Big Data in the Cloud.

We seek recommendations and innovative methods that can be successfully delivered to multidisciplines, providing us quality papers centered on Big Data in the Cloud and whose lessons learned will be transferable across disciplines to encourage inter-disciplinary research and funding activities essential for progressive research and development.

Papers will be peer reviewed by independent reviewers and selected on the basis of their quality and relevance to the topics of this special issue. The journal editors will make final decisions on the acceptance of the papers. A few best papers from CloudCom 2014 and Enterprise Security 2014 will be invited.

Link: http://www.journals.elsevier.com/future-generation-computer-systems/call-for-papers/fgcs-special-issue-big-data-in-the-cloud/

Submission Guidelines

Authors should prepare their manuscript according to the Guide for Authors available from the online submission page of the Future Generation Computer Systems at http://ees.elsevier.com/fgcs/. Authors should select “SI: Big Data in the Cloud” when they reach the “Article Type” step in the submission process.

Tentative schedule

Full manuscript due: May 31, 2015
Notification of the first review process: August 31, 2015
Revision due: November 15, 2015
Final acceptance notification: February 1, 2016
Final manuscript due: March 1, 2016
Publication date: Fall 2016 (Tentative)

Lead guest editor

Dr. Victor Chang
Leeds Beckett University, UK
ic.victor.chang at gmail.com

(If you make an enquiry, please state FGCS SI: Big Data in the Cloud in your email’s subject)

Guest editors

Dr. Muthu Ramachandran
Leeds Beckett University, UK
M.Ramachandran at leedsbeckett.ac.uk

Dr. Gary Wills
University of Southampton, UK
gbw at ecs.soton.ac.uk

Dr. Robert John Walters
University of Southampton, UK
rjw1 at ecs.soton.ac.uk

Dr. Chung-Sheng Li
IBM, US
csli at us.ibm.com